Practice Free NCP-AII Exam Online Questions
You’re working with a large dataset of microscopy images stored as individual TIFF files. The images are accessed randomly during a training job. The current storage solution is a single HDD. You’re tasked with improving data loading performance.
Which of the following storage optimizations would provide the GREATEST performance improvement in this specific scenario?
- A . Implementing data deduplication on the storage volume.
- B . Migrating the data to a large, sequential HDD.
- C . Replacing the HDD with a RAID 5 array of HDDs.
- D . Replacing the HDD with a single NVMe SSD.
- E . Compressing the TIFF files using a lossless compression algorithm.
D
Explanation:
Random access to numerous small files is a classic use case where SSDs excel due to their low latency. Replacing the HDD with an NVMe SSD (option D) will provide the most significant performance improvement. Data deduplication (A) saves storage space but doesn’t directly improve random access speed. Migrating to a sequential HDD (B) is counterproductive for random access. RAID 5 (C) provides some performance improvement but not as much as an SSD. Compression (E) can reduce storage space but adds overhead during decompression.
You are developing a CI/CD pipeline for deploying A1 models to a Kubernetes cluster with NVIDIA GPUs. Your pipeline includes steps to build, test, and deploy the model as a containerized application.
What is the recommended approach for ensuring that the application container image includes the correct NVIDIA drivers and libraries?
- A . Manually installing the NVIDIA drivers and libraries inside the container image during the build process.
- B . Using a base image provided by NVIDIA that includes the necessary drivers and libraries.
- C . Relying on the NVIDIA Container Toolkit to automatically install the drivers and libraries at runtime.
- D . Downloading the latest drivers from NVIDIA’s website and adding them to the container image.
- E . Configuring the cluster to download and configure the needed drivers just before deploying your application.
B
Explanation:
Using a base image provided by NVIDIA (such as those from NGC) is the recommended approach because it ensures that the container image includes the correct and compatible drivers and libraries, simplifying the build process and reducing potential compatibility issues. The NVIDIA Container Toolkit requires host drivers and handles the injection of the library into the container and not the building of the image.
You are running a Docker container with GPU support using ‘nvidia-docker run’. The containerized application unexpectedly fails to detect the GPU.
What is the most likely cause?
- A . The NVIDIA drivers are not installed on the host system.
- B . The Docker image does not include the CUDA toolkit.
- C . The Docker daemon is not configured to use the NVIDIA runtime.
- D . The application within the container is not linked against the CUDA libraries.
- E . The =gpus all’ flag was not specified when running the container.
E
Explanation:
When running containers that need GPU access, it’s essential to explicitly request the GPU resources. The ‘―gpus all’ or ‘―gpus device=..: flag passed to ‘docker run’ with the NVIDIA runtime allows the container access to the available GPUs. Without this flag, the container operates as if no GPUs are available.
Options A, B, C and D, while potentially problematic, are not the most likely cause if ‘nvidia-docker run’ was used previously.
You are setting up a multi-GPU AI server for deep learning. You want to ensure optimal inter-GPU communication.
Which of the following interconnect technologies would provide the BEST performance?
- A . PCle Gen3 x16
- B . PCle Gen4 x16
- C . NVLink
- D . Ethernet
- E . Infiniband
C
Explanation:
NVLink is designed specifically for high-bandwidth, low-latency inter-GPU communication, offering significantly better performance than PCIe or network connections for workloads that benefit from it. InfiniBand is suitable for node to node communication, while NVLink is for GPU to GPU on the same node.
You observe high latency and low bandwidth between two GPUs connected via an NVLink switch. You suspect a problem with the NVLink link itself.
Which of the following methods would be the most effective in diagnosing the physical NVLink link health?
- A . Using ‘iperf3’ to measure network throughput between the servers.
- B . Running a CUDA-aware memory bandwidth test specifically designed for NVLink.
- C . Examining system logs for NVLink-related error messages.
- D . Using ‘ping’ to check network connectivity between the servers.
- E . Physically inspecting the NVLink cables for damage.
B,C,E
Explanation:
A CUDA-aware memory bandwidth test can specifically measure the NVLink link’s performance. System logs can reveal hardware- level errors. Physical inspection can identify damaged cables. ‘iperf3’ and ‘pings are network-level tools and don’t directly test the NVLink link. Checking for error messages in System Logs also helps identify potential issues related to the NVLink switch and the link connections.